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| eman2:programs:e2tomoseg [2025/06/14 21:01] – steveludtke | eman2:programs:e2tomoseg [2025/06/14 21:05] (current) – steveludtke |
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| ===== Apply to Tomograms ===== | ===== Apply to Tomograms ===== |
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| | {{ http://blake.bcm.edu/dl/EMAN2/segment_out.png|Segmentation Result}} |
| Finally, open **Apply the neural network panel**. Choose the tomogram you used to generate the boxes in the **tomograms** box, choose the saved neural network file (not the "trainout_" file, which is only used for visualization), and set the output filename. You can change the number of threads to use by adjusting the **thread** option. Keep in mind that using more threads will consume more memory as the tomogram slices are read in at the same time. For example, processing a 1k x 1k x 512 downsampled tomogram on 10 cores would use ~5 GB of RAM. Processing an unscaled 4k x 4k x 1k tomogram would increase RAM usage to ~24 GB. When this process finishes, you can open the output file in your favourite visualization software to view the segmentation. | Finally, open **Apply the neural network panel**. Choose the tomogram you used to generate the boxes in the **tomograms** box, choose the saved neural network file (not the "trainout_" file, which is only used for visualization), and set the output filename. You can change the number of threads to use by adjusting the **thread** option. Keep in mind that using more threads will consume more memory as the tomogram slices are read in at the same time. For example, processing a 1k x 1k x 512 downsampled tomogram on 10 cores would use ~5 GB of RAM. Processing an unscaled 4k x 4k x 1k tomogram would increase RAM usage to ~24 GB. When this process finishes, you can open the output file in your favourite visualization software to view the segmentation. |
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| {{ http://blake.bcm.edu/dl/EMAN2/segment_out.png|Segmentation Result}} | |
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| To segment a different feature, just repeat the entire process for the each feature of interest. Make sure to use different file names (eg - _good2 and _bad2)! The trained network should generally work well on other tomograms using a similar specimen with similar microscope settings (clearly the A/pix value must be the same). | To segment a different feature, just repeat the entire process for the each feature of interest. Make sure to use different file names (eg - _good2 and _bad2)! The trained network should generally work well on other tomograms using a similar specimen with similar microscope settings (clearly the A/pix value must be the same). |
| Merging the results from multiple networks on a single tomogram can help resolve ambiguities, or correct regions which were apparently misassigned. For example, in the microtubule annotation shown above, the carbon edge is falsely recognized as a microtubule. An extra neural network can be trained to specifically recognize the carbon edge and its result can be competed against the microtubule annotation. A multi-level mask is produced after merging multiple annotation result in which the integer values in a voxel identify the type of feature the voxel contains. To merge multiple annotation results, simply run in the terminal: | Merging the results from multiple networks on a single tomogram can help resolve ambiguities, or correct regions which were apparently misassigned. For example, in the microtubule annotation shown above, the carbon edge is falsely recognized as a microtubule. An extra neural network can be trained to specifically recognize the carbon edge and its result can be competed against the microtubule annotation. A multi-level mask is produced after merging multiple annotation result in which the integer values in a voxel identify the type of feature the voxel contains. To merge multiple annotation results, simply run in the terminal: |
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| mergetomoseg.py <annotation #1> <annotation #2> <...> --output <output mask file> | <code> |
| | mergetomoseg.py <annotation #1> <annotation #2> <...> --output <output mask file> |
| | </code> |
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| ===== Tips in selecting training samples ===== | ===== Tips in selecting training samples ===== |
